Unit 14 of 15
Responsible Use and Deployment
- 5 lessons
- Videos 84 to 88
A model that scores well overall can still fail some groups, mislead its users or break when the data change. This unit covers fairness, interpretation, distribution shift, privacy and deployment.
Subgroup metrics need explicit denominators, and fairness criteria such as demographic parity and equalized odds can conflict when base rates differ. Permutation importance, partial dependence and SHAP values describe what a model relies on, not causal effects. Covariate, label and concept shift need different evidence, and a stress test checks robustness to a stated change. Data rights, membership inference, differential privacy and data poisoning are part of the same workflow. Deployment keeps preprocessing with the model, monitors drift and documents the intended use in a model card.
Every lesson will be one video. The videos are coming soon, and each lesson page already has the definitions and an example for every key term.
Fairness and Interpretation
Fairness and Subgroup Evaluation
How to compute subgroup metrics with their denominators and uncertainty, what base rates, selection rates, demographic parity, equality of opportunity and equalized odds compare, and why these criteria can conflict.
Video 84
Model Interpretation and Feature Importance
How permutation importance, partial dependence, individual conditional expectation curves and SHAP values describe a fitted model globally or locally, what each depends on, and why none measures a causal effect.
Video 85
Distribution Shift, Privacy and Security
Distribution Shift and Robustness
How covariate, label and concept shift change a model's risk, when importance weighting recovers the new risk, and what stress tests, out-of-distribution inputs, abstention and feedback loops mean for a deployed model.
Video 86
Privacy, Data Rights, and Model Security
Why a public download is not a dataset license and deleting names leaves personal data, how memorization enables membership inference, and what differential privacy, a privacy budget, data poisoning and adversarial inputs mean.
Video 87
Deployment and Documentation
Deployment, Monitoring, and Model Documentation
How an inference pipeline keeps preprocessing with the model, how schema validation, model versioning and rollback protect served predictions, and how drift monitoring, delayed labels and a model card support honest use.
Video 88